MEMES: Machine learning framework for Enhanced MolEcular Screening

15 July 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from large small-molecule drug library is evaluated for physical property such as the docking score against a target receptor. In real-life drug discovery experiments, the drug libraries are extremely large but still a minor representation of the essentially infinite chemical space, and evaluation of physical property for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening ("MEMES") based on Bayesian optimization is proposed for efficient sampling of chemical space. The proposed framework is demonstrated to identify 90% of top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.


Chemical space
Artificial Intelligence
machine Learning
Bayesian optimization
virtual screening
high throughput screening

Supplementary materials

Supplementary Material
Tables of performance of ExactMEMES and DeepMEMES, performance comparison of MEMES with Deep Docking, Figures of structure of top hits, distribution plots of binding affinities, distributions of molecular clusters, distributions of binding affinities of missed hits, fractions matched against sampled percentage, protein-ligand complexes and protein-ligand interactions, and supplementary discussions and methods.


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.